DocumentCode :
3472325
Title :
Fuzzy modeling and prediction of cylindricity error in BTA deep hole boring process
Author :
Al-Wedyan, H. ; Demirli, Kudret ; Bhat, Rama
Author_Institution :
Dept. of Mechanical & Industrial Eng., Concordia Univ., Montreal, Que., Canada
Volume :
2
fYear :
2004
fDate :
27-30 June 2004
Firstpage :
979
Abstract :
In this paper, first order Sugeno models are proposed to model the relationship between the different cutting parameters and the resulting cylindricity error. This gives the machine operator the opportunity to predict cylindricity for a given set of cutting parameters. Hence, the best parameters can be picked to achieve the best cylindricity. A second model shows that the cutting parameters should vary while drilling in order to reduce the whirling mode. As the amplitude of whirling is reduced, this leads to better cylindricity.
Keywords :
adaptive systems; boring; fuzzy neural nets; inference mechanisms; BTA deep hole boring process; cutting parameters; cylindricity error prediction; first order Sugeno models; fuzzy modeling; whirling mode reduction; Boring; Drilling; Feeds; Industrial engineering; Length measurement; Machining; Predictive models; Rough surfaces; Surface roughness; Vibrations;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Information, 2004. Processing NAFIPS '04. IEEE Annual Meeting of the
Print_ISBN :
0-7803-8376-1
Type :
conf
DOI :
10.1109/NAFIPS.2004.1337439
Filename :
1337439
Link To Document :
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